Multimodal knowledge graph completion (MKGC) aims to predict missing entities
in MKGs. Previous works usually share relation representation across
modalities. This results in mutual interference between modalities during
training, since for a pair of entities, the relation from one modality probably
contradicts that from another modality. Furthermore, making a unified
prediction based on the shared relation representation treats the input in
different modalities equally, while their importance to the MKGC task should be
different. In this paper, we propose MoSE, a Modality Split representation
learning and Ensemble inference framework for MKGC. Specifically, in the
training phase, we learn modality-split relation embeddings for each modality
instead of a single modality-shared one, which alleviates the modality
interference. Based on these embeddings, in the inference phase, we first make
modality-split predictions and then exploit various ensemble methods to combine
the predictions with different weights, which models the modality importance
dynamically. Experimental results on three KG datasets show that MoSE
outperforms state-of-the-art MKGC methods. Codes are available at
this https URL.

本文中提出的 MoSE 框架通过学习分解关系嵌入和集成预测结果，成功地解决了不同模式下关系表示共享的问题，以及不同模式下关系重要性的动态建模问题。

MoSE: 多模态知识图谱补全的模态切分与集成

MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion

In order to cope with the increasing demand for labeling data and privacy
issues with human detection, synthetic data has been used as a substitute and
showing promising results in human detection and tracking tasks. We participate
in the 7th Workshop on Benchmarking Multi-Target Tracking (BMTT), themed on
"How Far Can Synthetic Data Take us"? Our solution, PieTrack, is developed
based on synthetic data without using any pre-trained weights. We propose a
self-supervised domain adaptation method that enables mitigating the domain
shift issue between the synthetic (e.g., MOTSynth) and real data (e.g., MOT17)
without involving extra human labels. By leveraging the proposed multi-scale
ensemble inference, we achieved a final HOTA score of 58.7 on the MOT17 testing
set, ranked third place in the challenge.

使用合成数据以及自监督领域适应方法，得到了可行的人体检测和追踪结果，并在 MOT17 中获得了 58.7 的 HOTA 分数，排名第三。

PieTrack：基于合成数据训练和自监督域自适应的 MOT 解决方案

PieTrack: An MOT solution based on synthetic data training and  self-supervised domain adaptation

Predicting missing facts in a knowledge graph (KG) is a crucial task in
knowledge base construction and reasoning, and it has been the subject of much
research in recent works using KG embeddings. While existing KG embedding
approaches mainly learn and predict facts within a single KG, a more plausible
solution would benefit from the knowledge in multiple language-specific KGs,
considering that different KGs have their own strengths and limitations on data
quality and coverage. This is quite challenging, since the transfer of
knowledge among multiple independently maintained KGs is often hindered by the
insufficiency of alignment information and the inconsistency of described
facts. In this paper, we propose KEnS, a novel framework for embedding learning
and ensemble knowledge transfer across a number of language-specific KGs. KEnS
embeds all KGs in a shared embedding space, where the association of entities
is captured based on self-learning. Then, KEnS performs ensemble inference to
combine prediction results from embeddings of multiple language-specific KGs,
for which multiple ensemble techniques are investigated. Experiments on five
real-world language-specific KGs show that KEnS consistently improves
state-of-the-art methods on KG completion, via effectively identifying and
leveraging complementary knowledge.

本文提出了一种名为 KEnS 的新型框架，用于跨多个语言特定的知识图谱进行嵌入学习和集成知识传递，实现 KG 完成的改进。